Severity Analysis for Occupational Heat-related Injury Using the Multinomial Logit Model

Abstract

BackgroundWorkers are often exposed to hazardous heat due to their work environment, leading to various injuries. As a result of climate change, heat-related injuries (HRIs) are becoming more problematic. This study aims to identify critical contributing factors to the severity of occupational HRIs. MethodsThis study analyzed historical injury reports from the Occupational Safety and Health Administration (OSHA). Contributing factors to the severity of HRIs were identified using text mining and model-free machine learning methods. The Multinomial Logit Model (MNL) was applied to explore the relationship between impact factors and HRIs severity. ResultsThe results indicated a higher risk of fatal HRIs among middle-aged, older, and male workers, particularly in the construction, service, manufacturing, and agriculture industries. In addition, a higher heat index (HI), collapses, heart attacks, and fall accidents increased the severity of HRIs, while symptoms such as dehydration, dizziness, cramps, faintness, and vomiting reduced the likelihood of fatal HRIs. ConclusionsThe severity of HRIs was significantly influenced by factors like workers’ age, gender, industry type, HI, symptoms, and secondary injuries. The findings underscore the need for tailored preventive strategies and training across different worker groups to mitigate HRIs risks.